The type individuation problem

Lockean justifi cations of intellectual property postulate the appropriation of immaterial entities, in various contexts called types, patterns, designs, or technologies. It is widely believed that the ownership of such entities gives the owner a right to control their physical embodiments and prohibit imitation. For the prohibition to be meaningful, a condition identifying forbidden objects must be formulated. It must cover not only objects which are identical to some original artifact or its exact, ideal description, but also those which are only similar. This requires systematic answers to three questions: (1) which material structures and which of their subsets should be compared? (2) which of their characteristics should be compared? (3) how to combine these characteristics into a decision rule for token identification? There is no underlying empirical reality that could be independently consulted by individuals in order to incontestably answer these questions. Meanwhile constant evolution in technology and arts requires addressing them repeatedly. Consequently, intellectual property regimes must rely on political institutions incessantly dictating the scope of prohibition, and hence they cannot originate or exist in a prelegal state of nature.

From Goal Programming for Continuous Multi-Criteria Optimization to the Target Decision Rule for Mixed Uncertain Problems

Goal programming (GP) is applied to the discrete and continuous version of multi-criteria optimization. Recently, some essential analogies between multi-criteria decision making under certainty (M-DMC) and scenario-based one-criterion decision making under uncertainty (1-DMU) have been revealed in the literature. The aforementioned similarities allow the adjustment of GP to an entirely new domain. The aim of the paper is to create a new decision rule for mixed uncertain problems on the basis of the GP methodology. The procedure can be used by pessimists, optimists and moderate decision makers. It is designed for one-shot decisions. One of the significant advantages of the novel approach is related to the possibility to analyze neutral criteria, which are not directly taken into account in existing classical procedures developed for 1-DMU.

Naive Learning Through Probability Overmatching

Naïve Learning in a Binary Action, Social Network Environment In “Naïve Learning Through Probability Overmatching,” I. Arieli, Y. Babichenko, and M. Mueller-Frank consider an environment where privately informed agents select a binary action repeatedly observing the past actions of their neighbors in a social network. Rational inference has been shown to be exceedingly complex in this environment. Instead, this paper focuses on boundedly rational agents that form beliefs according to discretized DeGroot updating and apply a decision rule that assigns a (mixed) action to each belief. It is shown that naïve learning, where the long run actions of all agents are optimal given their pooled private information, can be achieved in any strongly connected network if beliefs satisfy a high level of inertia and the decision rule coincides with probability overmatching. The main difference to existing naïve learning results is that here it is shown to hold (1) for binary rather than uncountable action spaces and (2) even for network and information structures where Bayesian agents fail to learn.

Battery Charging in Collision Models with Bayesian Risk Strategies

We constructed a collision model where measurements in the system, together with a Bayesian decision rule, are used to classify the incoming ancillas as having either high or low ergotropy (maximum extractable work). The former are allowed to leave, while the latter are redirected for further processing, aimed at increasing their ergotropy further. The ancillas play the role of a quantum battery, and the collision model, therefore, implements a Maxwell demon. To make the process autonomous and with a well-defined limit cycle, the information collected by the demon is reset after each collision by means of a cold heat bath.

COVID-19 assessment in family practice—A clinical decision rule based on self-rated symptoms and contact history

The study aimed to evaluate the diagnostic accuracy of contact history and clinical symptoms and to develop decision rules for ruling-in and ruling-out SARS-CoV-2 infection in family practice. We performed a prospective diagnostic study. Consecutive inclusion of patients coming for COVID-PCR testing to 19 general practices. Contact history and self-reported symptoms served as index test. PCR testing of nasopharyngeal swabs served as reference standard. Complete data were available from 1141 patients, 605 (53.0%) female, average age 42.2 years, 182 (16.0%) COVID-PCR positive. Multivariable logistic regression showed highest odds ratios (ORs) for “contact with infected person” (OR 9.22, 95% CI 5.61–15.41), anosmia/ageusia (8.79, 4.89–15.95), fever (4.25, 2.56–7.09), and “sudden disease onset” (2.52, 1.55–4.14). Patients with “contact with infected person” or “anosmia/ageusia” with or without self-reported “fever” had a high probability of COVID infection up to 84.8%. Negative response to the four items “contact with infected person, anosmia/ageusia, fever, sudden disease onset” showed a negative predictive value (NPV) of 0.98 (95% CI 0.96–0.99). This was present in 446 (39.1%) patients. NPV of “completely asymptomatic,” “no contact,” “no risk area” was 1.0 (0.96–1.0). This was present in 84 (7.4%) patients. To conclude, the combination of four key items allowed exclusion of SARS-CoV-2 infection with high certainty. With the goal of 100% exclusion of SARS-CoV-2 infection to prevent the spread of SARS-CoV-2 to the population level, COVID-PCR testing could be saved only for patients with negative response in all items. The decision rule might also help for ruling-in SARS-CoV-2 infection in terms of rapid assessment of infection risk.